Mapper component for a neuro-linguistic behavior recognition system

ABSTRACT

Techniques are disclosed for generating a sequence of symbols based on input data for a neuro-linguistic model. The model may be used by a behavior recognition system to analyze the input data. A mapper component of a neuro-linguistic module in the behavior recognition system receives one or more normalized vectors generated from the input data. The mapper component generates one or more clusters based on a statistical distribution of the normalized vectors. The mapper component evaluates statistics and identifies statistically relevant clusters. The mapper component assigns a distinct symbol to each of the identified clusters.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of co-pending U.S. patent applicationSer. No. 16/456,456, filed on Jun. 28, 2019, which is a continuation ofU.S. patent application Ser. No. 14/569,034, filed on Dec. 12, 2014, nowU.S. Pat. No. 10,373,062, the entire content of each of which is hereinexpressly incorporated by reference for all purposes.

BACKGROUND Field

Embodiments described herein generally relate to data analysis systems,and more particularly, to generating symbols based on input data to beused in a neuro-linguistic behavioral recognition system.

Description of the Related Art

Many currently available surveillance and monitoring systems (e.g.,video surveillance systems, SCADA monitoring systems, and the like) aretrained to observe specific activities or patterns and alert anadministrator when an occurrence of a predefined activity or pattern isdetected. However, such systems require advance knowledge of whatactions and/or objects to observe. The activities may be hard-coded intounderlying applications or the system may train itself based on provideddefinitions. In other words, unless the underlying code includesdescriptions of certain behaviors, the system is incapable ofrecognizing such behaviors.

In addition, many monitoring systems, e.g., video surveillance systems,require a significant amount of computing resources, including processorpower, storage, and bandwidth. For example, typical video surveillancesystems require a large amount of computing resources per camera feedbecause of the typical size of video data. Given the cost of theresources, such systems are difficult to scale.

SUMMARY

One embodiment presented herein includes a method for generating asequence of symbols based on a stream of normalized vectors generatedfrom input data. The method generally includes receiving a normalizedvector of feature values generated from input data. Each feature valueis associated with one of a plurality of features. For each featurevalue in the normalized vector, a distribution of one or more clustersin a cluster space corresponding to one of the plurality of featuresassociated with the feature value is evaluated, and the feature value ismapped to one of the clusters based on the distribution.

Another embodiment presented herein includes a computer-readable storagemedium storing instructions, which, when executed on a processor,performs an operation for generating a sequence of symbols based on astream of normalized vectors generated from input data. The operationitself generally includes receiving a normalized vector of featurevalues generated from input data. Each feature value is associated withone of a plurality of features. For each feature value in the normalizedvector, a distribution of one or more clusters in a cluster spacecorresponding to one of the plurality of features associated with thefeature value is evaluated, and the feature value is mapped to one ofthe clusters based on the distribution.

Yet another embodiment presented herein includes a system having aprocessor and a memory storing one or more application programsconfigured to perform an operation for generating a sequence of symbolsbased on a stream of normalized vectors generated from input data. Theoperation itself generally includes receiving a normalized vector offeature values generated from input data. Each feature value isassociated with one of a plurality of features. For each feature valuein the normalized vector, a distribution of one or more clusters in acluster space corresponding to one of the plurality of featuresassociated with the feature value is evaluated, and the feature value ismapped to one of the clusters based on the distribution.

BRIEF DESCRIPTION OF THE DRAWINGS

So that the manner in which the above recited features of the presentdisclosure can be understood in detail, a more particular description ofthe disclosure, briefly summarized above, may be had by reference toembodiments, some of which are illustrated in the appended drawings. Itis to be noted, however, that the appended drawings illustrate onlyexemplary embodiments and are therefore not to be considered limiting ofits scope, may admit to other equally effective embodiments.

FIG. 1 illustrates an example computing environment for aneuro-linguistic behavior recognition system, according to oneembodiment.

FIG. 2 illustrates a system architecture of a neuro-linguistic behaviorrecognition system, according to one embodiment.

FIG. 3 illustrates a method for collecting sensor data for use in aneuro-lingustic behavior recognition system, according to oneembodiment.

FIG. 4 illustrates a method for updating a cluster distribution during alearning phase of a mapper component in a neuro-linguistic behaviorrecognition system, according to one embodiment.

FIG. 5 illustrates an example cluster distribution, according to oneembodiment.

FIG. 6 illustrates a method for sending symbols to a lexical analyzercomponent in a neuro-linguistic behavior recognition system, accordingto one embodiment.

To facilitate understanding, identical reference numerals have beenused, where possible, to designate identical elements that are common tothe figures. It is contemplated that elements and features of oneembodiment may be beneficially incorporated in other embodiments withoutfurther recitation.

DETAILED DESCRIPTION

Embodiments presented herein describe a behavior recognition system. Thebehavior recognition system may be configured with one or more datacollector components that collect raw data values from different datasources (e.g., video data, building management data, SCADA data). Forexample, a behavior recognition system may be configured for videosurveillance. The behavior recognition system may include a datacollector component that retrieves video frames in real-time, separatesforeground objects from background objects, and tracks foregroundobjects from frame-to-frame. The data collector component may normalizethe video frame data into numerical values (e.g., falling within a rangefrom 0 to 1 with respect to a given data type).

In one embodiment, the behavior recognition system includes aneuro-linguistic module that performs neural network-based linguisticanalysis on the collected data. Specifically, for each type of datamonitored by a sensor, the neuro-linguistic module creates and refines alinguistic model of the normalized data. That is, the neuro-linguisticmodule builds a grammar used to describe the normalized data. Thelinguistic model includes symbols that serve as building blocks for thegrammar. The neuro-linguistic module identifies combinations of symbolsto build a dictionary of words. Once the dictionary is built, theneuro-linguistic module identifies phrases that include variouscombinations of words in the dictionary. The behavior recognition systemuses such a linguistic model to describe what is being observed. Thelinguistic model allows the behavior recognition system to distinguishbetween normal and abnormal activity observed in the input data. As aresult, the behavior recognition system can issue alerts wheneverabnormal activity occurs.

To generate the linguistic model, a neuro-linguistic module receivesnormalized data values and organizes the data into clusters. Theneuro-linguistic module evaluates statistics of each cluster andidentifies statistically relevant clusters. Further, theneuro-linguistic module generates symbols, e.g., letters, correspondingto each statistically relevant cluster. Thus, input values mapping to agiven cluster may correspond to a symbol.

The neuro-linguistic module generates a lexicon, i.e., builds adictionary, of observed combinations of symbols, i.e., words, based on astatistical distribution of symbols identified in the input data.Specifically, the neuro-linguistic module may identify patterns ofsymbols in the input data at different frequencies of occurrence.Further, the neuro-linguistic module can identify statistically relevantcombinations of symbols at different lengths (e.g., from one-symbol to amaximum-symbol word length). The neuro-linguistic module may includesuch statistically relevant combinations of symbols in a dictionary usedto identify phrases for the linguistic model.

Using words from the dictionary, the neuro-linguistic module generatesphrases based on probabilistic relationships of each word occurring insequence relative to other words as additional data is observed. Forexample, the neuro-linguistic module may identify a relationship betweena given three-letter word that frequently appears in sequence with agiven four-letter word, and so on. The neuro-linguistic moduledetermines a syntax based on the identified phrases.

The syntax allows the behavior recognition system to learn, identify,and recognize patterns of behavior without the aid or guidance ofpredefined activities. Unlike a rules-based surveillance system, whichcontains predefined patterns of what to identify or observe, thebehavior recognition system learns patterns by generalizing input andbuilding behavior memories of what is observed. Over time, the behaviorrecognition system uses these memories to distinguish between normal andanomalous behavior reflected in observed data.

For instance, the neuro-linguistic module builds letters, words,phrases, and estimates an “unusualness score” for each identifiedletter, word, or phrase. The unusualness score (for a letter, word, orphrase observed in input data) provides a measure of how infrequentlythe letter, word, or phrase has occurred relative to past observations.Thus, the behavior recognition system may use the unusualness scores toboth measure how unusual a current syntax is, relative to a stable modelof symbols (i.e., letters), a stable model of words built from thesymbols (i.e., a dictionary) and a stable model of phrase built from thewords (i.e., a syntax)—collectively the neuro-linguistic model.

As the neuro-linguistic module continues to receive input data, theneuro-linguistic module may decay, reinforce, and generate the letters,words, and syntax models. In parlance with the machine learning field,the neuro-linguistic module “learns on-line” as new data is received andoccurrences a given type of input data either increases, decreases,appears, or disappears.

FIG. 1 illustrates components of a behavioral recognition system 100,according to one embodiment. As shown, the behavioral recognition system100 includes one or more input source devices 105, a network 110, andone or more computer systems 115. The network 110 may transmit datainput by the source devices 105 to the computer system 115. Generally,the computing environment 100 may include one or more physical computersystems 115 connected via a network (e.g., the Internet). Alternatively,the computer systems 115 may be cloud computing resources connected bythe network. Illustratively, the computer system 115 includes one ormore central processing units (CPU) 120, one or more graphics processingunits (GPU) 121, network and I/O interfaces 122, a storage 124 (e.g., adisk drive, optical disk drive, and the like), and a memory 123 thatincludes a sensor management module 130, a sensory memory component 135,and a machine learning engine 140. The storage 124 includes a modelrepository 145.

The CPU 120 retrieves and executes programming instructions stored inthe memory 123 as well as stores and retrieves application data residingin the storage 124. In one embodiment, the GPU 121 implements a ComputeUnified Device Architecture (CUDA). Further, the GPU 121 is configuredto provide general purpose processing using the parallel throughputarchitecture of the GPU 121 to more efficiently retrieve and executeprogramming instructions stored in the memory 123 and also to store andretrieve application data residing in the storage 124. The parallelthroughput architecture provides thousands of cores for processing theapplication and input data. As a result, the GPU 121 leverages thethousands of cores to perform read and write operations in a massivelyparallel fashion. Taking advantage of the parallel computing elements ofthe GPU 121 allows the behavior recognition system 100 to better processlarge amounts of incoming data (e.g., input from a video and/or audiosource). As a result, the behavior recognition system 100 may scale withrelatively less difficulty.

The sensor management module 130 provides one or more data collectorcomponents. Each of the collector components is associated with aparticular input data source, e.g., a video source, a SCADA (supervisorycontrol and data acquisition) source, an audio source, etc. Thecollector components retrieve (or receive, depending on the sensor)input data from each source at specified intervals (e.g., once a minute,once every thirty minutes, once every thirty seconds, etc.). The sensormanagement module 130 controls the communications between the datasources. Further, the sensor management module 130 normalizes input dataand sends the normalized data to the sensory memory component 135.

The sensory memory component 135 is a data store that transfers largevolumes of data from the sensor management module 130 to the machinelearning engine 140. The sensory memory component 135 stores the data asrecords. Each record may include an identifier, a timestamp, and a datapayload. Further, the sensory memory component 135 aggregates incomingdata in a time-sorted fashion. Storing incoming data from each of thedata collector components in a single location where the data may beaggregated allows the machine learning engine 140 to process the dataefficiently. Further, the computer system 115 may reference data storedin the sensory memory component 135 in generating alerts for anomalousactivity. In one embodiment, the sensory memory component 135 may beimplemented in via a virtual memory file system in the memory 123. Inanother embodiment, the sensory memory component 135 is implementedusing a key-value share.

The machine learning engine 140 receives data output from the sensormanagement module 135. Generally, components of the machine learningengine 140 generate a linguistic representation of the normalizedvectors. As described further below, to do so, the machine learningengine 140 clusters normalized values having similar features andassigns a distinct symbol to each cluster. The machine learning engine140 may then identify recurring combinations of symbols (i.e., words) inthe data. The machine learning engine 140 then similarly identifiesrecurring combinations of words (i.e., phrases) in the data.

Note, however, FIG. 1 illustrates merely one possible arrangement of thebehavior recognition system 100. For example, although the input datasources 105 are shown connected to the computer system 115 via network110, the network 110 is not always present or needed (e.g., an inputsource such as a video camera may be directly connected to the computersystem 115).

FIG. 2 illustrates a system architecture of the behavior recognitionsystem, according to one embodiment. As shown, the sensor managementmodule 130 and the machine learning engine 140 communicate via apersistence layer 210.

The persistence layer 210 includes data stores that maintain informationused by components of the computer system 115. For example, thepersistence layer 210 includes data stores that maintain informationdescribing properties of the data collector modules 202, systemproperties (e.g., serial numbers, available memory, available capacity,etc. of the computer system 115), and properties of the source driver(e.g., active plug-ins 118, active sensors associated with each datasource, normalization settings, etc.). Other data stores may maintainlearning model information, system events, and behavioral alerts. Inaddition, the sensory memory component 135 resides in the persistencelayer 210.

The machine learning engine 140 itself includes a neuro-linguisticmodule 215 and a cognitive module 225. The neuro-linguistic module 215performs neural network-based linguistic analysis of normalized inputdata to build a neuro-linguistic model of the observed input data. Thebehavior recognition system can use the linguistic model to describesubsequently observed activity. However, rather than describing theactivity based on predefined objects and actions, the neuro-linguisticmodule 215 develops a custom language based on symbols, words, andphrases generated from the input data. As shown, the neuro-linguisticmodule 215 includes a data transactional memory (DTM) component 216, aclassification analyzer component 217, a mapper component 218, a lexicalanalyzer component 219, and a perceptual associative memory (PAM)component 220.

In one embodiment, the DTM component 216 retrieves the normalizedvectors of input data from the sensory memory component 135 and stagesthe input data in the pipeline architecture provided by the GPU 121. Theclassification analyzer component 217 evaluates the normalized dataorganized by the DTM component 216 and maps the data on a neuralnetwork. In one embodiment, the neural network is a combination of aself-organizing map (SOM) and an adaptive resonance theory (ART)network.

The mapper component 218 clusters the data streams based on valuesoccurring repeatedly in association with one another. Further, themapper component 218 generates a set of clusters for each input feature.For example, assuming that the input data corresponds to video data,features may include location, velocity, acceleration, etc. The mappercomponent 218 would generate separate sets of clusters for each of thesefeatures. The mapper component 218 identifies symbols (i.e., builds analphabet of letters) based on the clustered input data. Specifically,the mapper component 218 determines a statistical distribution of datain each cluster. For instance, the mapper component 218 determines amean, variance, and standard deviation for the distribution of values inthe cluster. The mapper component 218 also updates the statistics asmore normalized data is received. Further, each cluster may beassociated with a statistical significance score. The statisticalsignificance for a given cluster increases as more data is receivedwhich maps to that cluster. In addition, the mapper component 218 decaysthe statistical significance of the cluster as the mapper component 218observes data mapping to the cluster less often over time.

In one embodiment, the mapper component 218 assigns a set of symbols toclusters having statistical significance. A cluster has statisticalsignificance if a threshold amount of input data mapping to that clusteris exceeded. A symbol may be described as a letter of an alphabet usedto create words used in the neuro-linguistic analysis of the input data.A symbol provides a “fuzzy” representation of the data belonging to agiven duster.

Further, the mapper component 218 is adaptive. That is, the mappercomponent 218 may identify new symbols corresponding to new dustersgenerated from the normalized data, as such dusters are reinforced overtime (resulting in such dusters reaching a level statisticalsignificance relative to the other dusters that emerge from the inputdata). The mapper component 218 learns on-line and may merge similarobservations to a more generalized duster. The mapper component 218 mayassign a set of distinct symbols to the resulting cluster.

Once a cluster has reached statistical significance (i.e., data observedas mapping to that cluster has reached a threshold amount of points),the mapper component 219 begins sending corresponding symbols to thelexical analyzer component 219 in response to normalized data that mapsto that cluster. In one embodiment, the mapper component 218 limitssymbols that can be sent to the lexical component 219 to the moststatistically significant clusters. In practice, outputting symbols(i.e., letters) assigned to the top thirty-two clusters has shown to beeffective. However, other amounts may also prove effective, such as thetop sixty-four or 128 most frequently recurring clusters. Note, overtime, the most frequently observed symbols may change as clustersincrease (or decrease) in statistical significance. As such, it ispossible for a given cluster to lose statistical significance. Overtime, thresholds for statistical significance of a cluster can increase,and thus, if the amount of observed data mapping to a given clusterfails to meet a threshold, then the cluster loses statisticalsignificance.

In one embodiment, the mapper component 218 evaluates an unusualnessscore for each symbol. The unusualness score is based on the frequencyof a given symbol relative to other symbols observed in the input datastream, over time. The unusualness score may increase or decrease overtime as the neuro-linguistic module 215 receives additional data.

The mapper component 218 sends a stream of the symbols (e.g., letters),timestamp data, unusualness scores, and statistical data (e.g., arepresentation of the cluster associated with a given symbol) to thelexical analyzer component 219. The lexical analyzer component 219builds a dictionary based on symbols output from the mapper component218. In practice, the mapper component 218 may need approximately 5000observations (i.e., normalized vectors of input data) to generate astable alphabet of symbols.

The lexical analyzer component 219 builds a dictionary that includescombinations of co-occurring symbols, e.g., words, from the symbolstransmitted by the mapper component 218. The lexical analyzer component219 identifies repeating co-occurrences of letters output from themapper component 218 and calculates frequencies of the co-occurrencesthroughout the symbol stream. The combinations of symbols maysemantically represent a particular activity, event, etc.

In one embodiment, the lexical analyzer component 219 limits the lengthof words in the dictionary to allow the lexical analyzer component 219to identify a number of possible combinations without adverselyaffecting the performance of the computer system 115. Further, thelexical analyzer component 219 may use level-based learning models toanalyze symbol combinations and learn words. The lexical analyzercomponent 219 learns words up through a maximum symbol combinationlength at incremental levels, i.e., where one-letter words are learnedat a first level, two-letter words are learned at a second level, and soon. In practice, limiting a word to a maximum of five or six symbols(i.e., learning at a maximum of five or six levels) has shown to beeffective.

Like the mapper component 218, the lexical analyzer component 219 isadaptive. That is, the lexical analyzer component 219 may learn andgenerate words in the dictionary over time. The lexical analyzercomponent 219 may also reinforce or decay the statistical significanceof words in the dictionary as the lexical analyzer component 219receives subsequent streams of symbols over time. Further, the lexicalanalyzer component 219 may determine an unusualness score for each wordbased on how frequently the word recurs in the data. The unusualnessscore may increase or decrease over time as the neuro-linguistic module215 processes additional data.

In addition, as additional observations (i.e., symbols) are passed tothe lexical analyzer component 219 and identified as a being part of agiven word, the lexical analyzer component 219 may determine that theword model has matured. Once a word model has matured, the lexicalanalyzer component 219 may output observations of those words in themodel to the PAM component 219. In one embodiment, the lexical analyzercomponent 219 limits words sent to the PAM component 320 to the moststatistically significant words. In practice, for each single sample,outputting occurrences of the top thirty-two of the most frequentlyoccurring words has shown to be effective (while the most frequentlyoccurring words stored in the models can amount to thousands of words).Note, over time, the most frequently observed words may change as theobservations of incoming letters change in frequency (or as new lettersemerge by the clustering of input data by the mapper component 218.

Once the lexical analyzer component 219 has built the dictionary (i.e.,identifies words that have reached a predefined statisticalsignificance), the lexical analyzer component 219 sends occurrences ofwords subsequently observed in the input stream to the PAM component220. The PAM component 220 builds a syntax of phrases from the wordsoutput by the lexical analyzer component 219. In practice, lexicalanalyzer component 219 may build a useful dictionary of words afterreceiving approximately 15,000 observations (i.e., input letters fromthe mapper component 218).

The PAM component 220 identifies a syntax of phrases based on thesequence of words output from the lexical analyzer component 219.Specifically, the PAM component 220 receives the words identified by thelexical analyzer component 219 generates a connected graph, where thenodes of the graph represent the words, and the edges represent arelationship between the words. The PAM component 220 may reinforce ordecay the links based on the frequency that the words are connected withone another in a data stream.

Similar to the mapper component 218 and the lexical analyzer component219, the PAM component 220 determines an unusualness score for eachidentified phrase based on how frequently the phrase recurs in thelinguistic data. The unusualness score may increase or decrease overtime as the neuro-linguistic module 215 processes additional data.

Similar to the lexical analyzer component 219, the PAM component 220 maylimit the length of a given phrase to allow the PAM component 220 to beable to identify a number of possible combinations without adverselyaffecting the performance of the computer system 115.

The PAM component 220 identifies syntax phrases over observations ofwords output from the lexical analyzer component 219. As observations ofwords accumulate, the PAM component 220 may determine that a givenphrase has matured, i.e., a phrase has reached a measure of statisticalsignificance. The PAM component 220 then outputs observations of thatphrase to the cognitive module 225. The PAM component 220 sends datathat includes a stream of the symbols, words, phrases, timestamp data,unusualness scores, and statistical calculations to the cognitive module325. In practice, the PAM component 220 may obtain a meaningful set ofphrases after observing about 5000 words from the lexical analyzercomponent 219.

After maturing, the generated letters, words, and phrases form a stableneuro-linguistic model of the input data that the computer system 115uses to compare subsequent observations of letters, words, and phrasesagainst the stable model. The neuro-linguistic module 215 updates thelinguistic model as new data is received. Further, the neuro-linguisticmodule 215 may compare a currently observed syntax to the model. Thatis, after building a stable set of letters, the neuro-linguistic module215 may build a stable model of words (e.g., a dictionary). In turn, theneuro-linguistic module 215 may be used to build a stable model ofphrases (e.g., a syntax). Thereafter, when the neuro-linguistic module215 receives subsequently normalized data, the module 215 can output anordered stream of symbols, words, and phrases, all of which can becompared to the stable model to identify interesting patterns or detectdeviations occurring in the stream of input data.

As shown, the cognitive module 226 includes a workspace 226, a semanticmemory 230, codelet templates 235, episodic memory 240, long term memory245, and an anomaly detection component 250. The semantic memory 230stores the stable neuro-linguistic model described above, i.e., a stablecopy from the mapper component 218, lexical analyzer component 219, andthe PAM component 220.

In one embodiment, the workspace 226 provides a computational engine forthe machine learning engine 140. The workspace 226 performs computations(e.g., anomaly modeling computations) and stores intermediate resultsfrom the computations.

The workspace 226 retrieves the neuro-linguistic data from the PAMcomponent 220 and disseminates this data to different portions of thecognitive module 225 as needed.

The episodic memory 240 stores linguistic observations related to aparticular episode in the immediate past and may encode specificdetails, such as the “what” and the “when” of a particular event.

The long-term memory 245 stores generalizations of the linguistic datawith particular episodic details stripped away. In this way, when a newobservation occurs, memories from the episodic memory 240 and thelong-term memory 245 may be used to relate and understand a currentevent, i.e., the new event may be compared with past experience (asrepresented by previously observed linguistic data), leading to bothreinforcement, decay, and adjustments to the information stored in thelong-term memory 245, over time. In a particular embodiment, thelong-term memory 245 may be implemented as an ART network and asparse-distributed memory data structure. Importantly, however, thisapproach does not require events to be defined in advance.

The codelet templates 235 provide a collection of executable codelets,or small pieces of code that evaluate different sequences of events todetermine how one sequence may follow (or otherwise relate to) anothersequence. The codelet templates 325 may include deterministic codeletsand stochastic codelets. More generally, a codelet may detectinteresting patterns from the linguistic representation of input data.For instance, a codelet may compare a current observation (i.e., acurrent phrase instance with what has been observed in the past) withpreviously observed activity.

The anomaly detection component 250 evaluates unusualness scores sent bythe neuro-linguistic module 215 to determine whether to issue an alertin response to some abnormal activity indicated by the unusualnessscores. Specifically, the anomaly detection component 250 is providesprobabilistic histogram models, e.g., an unusual lexicon score model, anunusual syntax score model, and an anomaly model, which represent theunusualness scores. The unusual lexicon score model and unusual syntaxscore model are generated based on unusualness scores sent from thelexical analyzer component 219 and the PAM component 220. The anomalymodel receives input percentiles from the unusual lexicon score modeland unusual syntax score model and generates an absolute unusualnessscore based on the percentiles. The anomaly detection component 250evaluates the scores and determines whether to send an alert based on agiven score. The anomaly detection component 250 may send alert data toan output device, where an administrator may view the alert, e.g., via amanagement console.

The cognitive module 225 performs learning analysis on the linguisticcontent delivered to semantic memory 230 (i.e., the identified symbols,words, phrases) by comparing new observations to the learned patterns inthe stable neuro-linguistic model kept in semantic memory 230 and thenestimating the rareness of these new observations.

Specifically, the anomaly detection component 250 evaluates theunusualness scores of each of the symbols, words, and phrases toidentify abnormal occurrences in the observed data. Once an anomalousobservation has been identified, the anomaly component may issue analert (e.g., notify an administrator or user of the computer system115).

FIG. 3 illustrates a method 300 for collecting sensor data for use in aneuro-lingustic behavior recognition system, according to oneembodiment. More specifically, method 300 describes a method for a datacollector to retrieve data from an associated input device and send thedata to the neuro-linguistic module 215. For this example, assume that adata collector module 202 is a video source capturing image data at agiven frame rate. Of course, a variety of data collector components 202can be used.

Method 300 begins at step 305, where the data collector module 202retrieves (or receives) data from the source input device. In this case,the data collector module 202 may retrieve video frames from a videosource, such as a video camera positioned to observe a particularlocation, such as a hotel lobby. Further, the data collector module 202identifies data values to send to the sensory memory component 135. Todo so, the data collector module 202 may evaluate the video frames toseparate foreground objects from background objects, measure appearanceand kinematic information of the identified foreground objects, andtrack foreground objects moving across the scene (i.e., the field ofview of the camera). As a result, the data collector module 202generates a set of data values characterizing appearance and kinematicaspects of the objects depicted in video frames.

At step 310, the data collector module 202 normalizes each data value toa numerical value falling within a range, e.g., between 0 to 1,inclusive, relative to the type of that data value. For example, valuesassociated with kinematic features are normalized from 0 to 1 relativeto other values associated with kinematic features. Doing so convertseach value to a common format and allows the neuro-linguistic module 215to recognize recurring events in the video stream.

After normalizing the values, at step 315, the data collector module 202identifies additional data associated with the normalized values, suchas a timestamp of a given value, an average associated with the datatype (e.g., kinematic features, appearance features, location, position,etc.) of the value, and historical high and low values for that datatype. Doing so allows the data collector module 202 to readjust thenormalization in the event that the video source is modified.Specifically, the data collector module 202 references the identifiedhistorical values and averages to readjust the normalization.

At step 320, the data collector module 202 sends a vector of thenormalized values and associated data to the sensory memory component135. As stated, the sensory memory component 135 stores the normalizedvalues and associated data. The neuro-linguistic module 215 may thenretrieve the normalized values from the sensory memory component 135 andperform linguistic analysis thereafter.

FIG. 4 illustrates a method 400 for updating a cluster distributionduring a learning phase of the mapper component 218, according to oneembodiment. Specifically, during the learning phase, the mappercomponent 218 generates clusters based on the normalized vectors sentfrom the sensory memory component 135. The mapper component 218generates these clusters on a neural network, e.g., an adaptiveresonance theory (ART) network. Further, during this phase, the mappercomponent 218 identifies statistically significant dusters, i.e.,dusters having data that has been observed to have a threshold amount ofpoints.

Method 400 begins at step 405, where the mapper component 218 receives anormalized vector of feature values. As stated, each vector includesvalues associated with features of the input data. For example, featuresfor video data may include an x, y location, velocity, acceleration,etc. Each feature is associated with a distinct cluster space. That is,the mapper component 218 generates a set of clusters for each clusterindependent of other clusters. In one embodiment, each feature value isnormalized to a value between 0 and 1, inclusive. Further, each valuemaps to a point in the feature space.

For each feature value in the normalized vector, the mapper component218 performs the following steps. At step 410, the mapper component 218evaluates a current cluster distribution for the associated featurespace. That is, the mapper component 218 evaluates point distributionstatistics for each cluster, such as a mean, a variance, and a standarddeviation. At initialization of the mapper component 218, each featurespace includes one cluster. The first value observed for that featurespace maps to that cluster and serves as the mean.

At step 415, the mapper component 218 maps the feature value to acorresponding duster. Generally, the feature value maps to a givenduster if the value falls within the variance of that cluster. If thefeature value does not map to a particular cluster, the mapper component218 may generate a new cluster and map the value to that cluster. Oncemapped, the mapper component 218 updates the cluster distribution. Basedon the updated distribution, the mapper component 218 can determinewhether a cluster has become statistically significant. That is, if themapper component 218 observes a threshold amount of feature valuesmapping to a given cluster, then the cluster has statisticalsignificance. In such a case, the mapper component 218 can output asymbol associated with the cluster when subsequently observed. In oneembodiment, the mapper component 218 may score the statisticalsignificance based on an amount of observations of feature valuesmapping to a given cluster. FIG. 5 illustrates an example clusterdistribution, according to one embodiment. Illustratively, the clusterdistribution includes a cluster A and a cluster B along a 0 to 1 valuerange (inclusive). Cluster A represents a distribution of valuesrelatively closer to 0, while cluster B represents a distribution ofvalues relatively closer to 1. Each cluster includes a mean value,represented by the lines 505 ₁₋₂. Further, each cluster includes anumber of values represented by the lines 510 ₁₋₂, each of whichrepresent a value separate standard deviations from the mean.

In this example distribution, assume that cluster B has reachedstatistical significance. In such a case, when the mapper component 218subsequently observes feature values that map to cluster B, the mappercomponent 218 outputs one of a set of symbols associated with cluster Bto the lexical analyzer component 219. The symbol output to the lexicalanalyzer component 219 depends on where the value maps within cluster B.In one embodiment, a value that maps near (based on a degree offuzziness) the mean value corresponds to a distinct symbol. Further, avalue that maps near (based on a degree of fuzziness) one of thestandard deviation values corresponds to a distinct symbol. In thiscase, cluster B is associated with five symbols.

FIG. 6 illustrates a method 600 for sending symbols to the lexicalanalyzer component 219, according to one embodiment. More specifically,method 600 describes outputting symbols and updating clusters as themapper component 218 subsequently receives normalized vectors. Method600 begins at step 605, where the mapper component 218 receives anormalized vector of input feature values. As stated, the normalizedvector includes input values within a range of 0 to 1, inclusive.

For each feature value, the mapper component 218 performs the followingsteps. At step 610, the mapper component 218 evaluates a current clusterdistribution of the associated feature space (i.e., the mean, variance,standard deviation, etc.). Doing so allows the mapper component 218 todetermine which cluster to map the feature value to.

At step 615, the mapper component 218 maps the feature value to acorresponding cluster. Once mapped, the mapper component 218 updates thedistribution of that cluster, such as the mean, variance, and standarddeviation. Further, the mapper component 218 uses the updateddistribution to determine whether the cluster should be merged withanother (e.g., if clusters begin to overlap with one another based onthe updated distribution). The mapper component 218 also uses theupdated distribution to determine whether to create additional cluster.Note, it is possible that a feature value does not map to any particularcluster. In such a case, the mapper component 218 can report such aninstance to the lexical analyzer component 219 as unknown. In addition,the mapper component 218 determines whether the corresponding cluster isstatistically significant based on the updated distribution (at step625). In one embodiment, the mapper component 218 may score thestatistical significance based on the amount of observations of featurevalues mapping to a given cluster.

If the cluster is statistically significant, the mapper component 218outputs a corresponding symbol to the lexical analyzer component 219. Asstated, the mapper component 218 determines the corresponding symbolrelative to where the feature value is mapped to the cluster (e.g.,relative to the mean, standard deviation). However, at step 635, if thefeature value maps to a cluster that is not statistically significant(or if the feature value does not map to a cluster at all), then themapper component 218 outputs the feature value as unknown to the lexicalanalyzer component 219.

Further, the mapper component 218 sends, to the lexical analyzercomponent 219, additional statistics to accompany the symbol, such astimestamp information, an unusualness score, frequency of occurrence,and the like. In turn, the lexical analyzer component 219 continues theneuro-linguistic analysis of the input by building a dictionary of wordsby identifying combinations of the symbols. The lexical analyzercomponent 219 may then output the dictionary of words to the PAMcomponent 220. The PAM component 220 identifies phrases based onstatistical frequencies of words appearing along with one another. Onceidentified, the PAM component 220 outputs the linguistic information(i.e., symbols, words, and phrases) to the cognitive module 220. Asstated, the cognitive module 220 analyzes the linguistic information tolearn and analyze normal and abnormal activity sent to the behaviorrecognition system.

One embodiment of the present disclosure is implemented as a programproduct for use with a computer system. The program(s) of the programproduct defines functions of the embodiments (including the methodsdescribed herein) and can be contained on a variety of computer-readablestorage media. Examples of computer-readable storage media include (i)non-writable storage media (e.g., read-only memory devices within acomputer such as CD-ROM or DVD-ROM disks readable by an optical mediadrive) on which information is permanently stored; (ii) writable storagemedia (e.g., floppy disks within a diskette drive or hard-disk drive) onwhich alterable information is stored. Such computer-readable storagemedia, when carrying computer-readable instructions that direct thefunctions of the present disclosure, are embodiments of the presentdisclosure. Other examples media include communications media throughwhich information is conveyed to a computer, such as through a computeror telephone network, including wireless communications networks.

In general, the routines executed to implement the embodiments of thepresent disclosure may be part of an operating system or a specificapplication, component, program, module, object, or sequence ofinstructions. The computer program of the present disclosure iscomprised typically of a multitude of instructions that will betranslated by the native computer into a machine-readable format andhence executable instructions. Also, programs are comprised of variablesand data structures that either reside locally to the program or arefound in memory or on storage devices. In addition, various programsdescribed herein may be identified based upon the application for whichthey are implemented in a specific embodiment of the disclosure.However, it should be appreciated that any particular programnomenclature that follows is used merely for convenience, and thus thepresent disclosure should not be limited to use solely in any specificapplication identified and/or implied by such nomenclature.

As described, embodiments herein provide techniques for generatingsymbols from data originating from an input source (e.g., video source,SCADA source, network security source, etc.) via a neuro-linguisticbehavior recognition system. Once generated, the behavior recognitionsystem uses the symbols to build a dictionary of words and establish asyntax, which forms the basis for a linguistic model used to describeinput data observed by the behavior recognition system. The behaviorrecognition system analyzes and learns behavior based on the linguisticmodel to distinguish between normal and abnormal activity in observeddata. Advantageously, this approach does not relying on predefinedpatterns to identify behaviors and anomalies but instead learns patternsand behaviors by observing a scene and generating information on what itobserves.

While the foregoing is directed to embodiments of the presentdisclosure, other and further embodiments of the disclosure may bedevised without departing from the basic scope thereof, and the scopethereof is determined by the claims that follow.

1. A method, comprising: receiving a normalized vector of featurevalues, each feature value from the normalized vector of feature valuesbeing calculated based on a feature from a plurality of features; foreach feature value from the normalized vector of feature values: mappingthat feature value to a first single cluster from a plurality ofclusters associated with that feature from the plurality of features,updating a distribution of the plurality of clusters based on themapping, to produce an updated distribution of the plurality ofclusters, and determining, based on the updated distribution of theplurality of clusters, whether to merge the plurality of clusters with afurther cluster from a cluster space associated with the plurality offeatures; determining a symbol for a statistically significant clusterfrom the plurality of clusters; and causing transmission of the symbolto a behavior recognition system.
 2. The method of claim 1, wherein thenormalized vector of feature values is a first normalized vector offeature values and the updated distribution of the plurality of clustersis a first updated distribution of the plurality of clusters, the methodfurther comprising: receiving a second normalized vector of featurevalues; and for each feature value from the second normalized vector offeature values: mapping that feature value to a second single clusterfrom the plurality of clusters, updating the first updated distributionof the plurality of clusters based on the mapping, to produce a secondupdated distribution of the plurality of clusters, and in response todetermining, based on the second updated distribution of the pluralityof clusters, that the second single cluster has statisticalsignificance, outputting a symbol from a plurality of symbols associatedwith the second single cluster.
 3. The method of claim 1, furthercomprising: determining, based on the distribution of the plurality ofclusters, that at least two clusters from the plurality of clustersoverlap in the cluster space, and merging the at least two clusters inresponse to determining that the at least two clusters overlap in thecluster space.
 4. The method of claim 1, wherein a statisticalsignificance of each cluster from the plurality of clusters isdetermined based on a statistical significance score that indicates anumber of feature values from the normalized vector of feature valuesthat map to that cluster over time.
 5. The method of claim 4, furthercomprising, for each cluster from the plurality of clusters, in responseto determining that feature values mapping to that cluster have not beenreceived after a period based on a function of time, decaying thestatistical significance of that cluster.
 6. The method of claim 1,wherein statistics associated with each cluster from the plurality ofclusters include a mean, a variance, and a standard deviation.
 7. Themethod of claim 1, wherein each feature value in the normalized vectorof feature values is within a range of 0 and 1, inclusive.
 8. Acomputer-readable storage medium storing instructions to cause aprocessor to: receive a normalized vector of feature values generatedfrom input data, each feature value from the normalized vector offeature values being calculated based on a feature from a plurality offeatures; for each feature value from the normalized vector of featurevalues: evaluate a distribution of a plurality of clusters in a clusterspace associated with that feature from the plurality of features thatis associated with the feature value, update the distribution of theplurality of clusters based on a mapping of that feature value to afirst single cluster from the plurality of clusters, to produce anupdated distribution of the plurality of clusters, and determine, basedon the updated distribution of the plurality of clusters, whether tomerge the plurality of clusters with a further cluster from the clusterspace; determine a symbol for a statistically significant cluster fromthe plurality of clusters; and cause transmission of the symbol to abehavior recognition system.
 9. The computer-readable medium of claim 8,wherein the normalized vector of feature values is a first normalizedvector of feature values, and the updated distribution of the pluralityof clusters is a first updated distribution of the plurality ofclusters, the medium further storing instructions to cause the processorto: receive a second normalized vector of feature values; and for eachfeature value in the second normalized vector of feature values: mapthat feature value to a second single cluster from the plurality ofclusters, update the first updated distribution of the plurality ofclusters based on the mapping, to produce a second updated distributionof the plurality of clusters, and upon determining, based on the secondupdated distribution of the plurality of clusters, that the secondsingle cluster has statistical significance, outputting a symbol from aplurality of symbols associated with the second single cluster.
 10. Thecomputer-readable medium of claim 8, further storing instructions tocause the processor to: determine, based on the evaluated distributionof the plurality of clusters, that at least two clusters from theplurality of clusters overlap in the cluster space, and merge the atleast two clusters in response to determining that the at least twoclusters overlap in the cluster space.
 11. The computer-readable mediumof claim 8, wherein a statistical significance of each cluster from theplurality of clusters is determined based on a statistical significancescore that indicates a number of feature values from the normalizedvector of feature values that map to that cluster over time.
 12. Thecomputer-readable medium of claim 11, further storing instructions tocause the processor to, for each cluster from the plurality of clustersand in response to determining that feature values mapping to thecluster have not been received after a period based on a function oftime, decay the statistical significance of that cluster.
 13. Thecomputer-readable medium of claim 8, wherein statistics associated witheach cluster from the plurality of clusters include a mean, variance,and standard deviation.
 14. The computer-readable medium of claim 8,wherein each feature value in the normalized vector of feature values iswithin a range of 0 and 1, inclusive.
 15. A system, comprising: aprocessor; and a memory storing instructions to cause the processor to:receive a normalized vector of feature values, each feature value fromthe normalized vector of feature values being calculated based on afeature from a plurality of features; for each feature value from thenormalized vector of feature values: evaluate a distribution of aplurality of clusters is associated with that feature value, map thatfeature value to a first single cluster from the plurality of clustersbased on the distribution, update the distribution of the plurality ofclusters based on the mapping, to produce an updated distribution of theplurality of clusters, and determine, based on the updated distributionof the plurality of clusters, whether to merge the plurality of clusterswith a further cluster from the cluster space; and identify a symbol fora statistically significant cluster from the plurality of clusters. 16.The system of claim 15, wherein the normalized vector of feature valuesis a first normalized vector of feature values, and the updateddistribution of the plurality of clusters is a first updateddistribution of the plurality of clusters, the memory further storinginstructions to cause the processor to: receive a second normalizedvector of feature values generated from second input data; and for eachfeature value from the second normalized vector of feature values: mapthat feature value to a second single cluster from the plurality ofclusters, update the first updated distribution of the plurality ofclusters based on the mapping, to produce a second updated distributionof the plurality of clusters, and upon determining, based on the secondupdated distribution of the plurality of clusters, that the secondsingle cluster has statistical significance, output a symbol from aplurality of symbols associated with the second single cluster.
 17. Thesystem of claim 15, wherein the memory further stores instructions tocause the processor to: determine, based on the evaluated distributionof the plurality of clusters, that at least two clusters from theplurality of clusters overlap in the cluster space, and merge the atleast two clusters in response to determining that the at least twoclusters overlap in the cluster space.
 18. The system of claim 15,wherein a statistical significance of each cluster from the plurality ofclusters is determined based on a statistical significance score thatindicates a number of feature values from the normalized vector offeature values that map to that cluster over time.
 19. The system ofclaim 15, wherein statistics associated with each cluster from theplurality of clusters include a mean, variance, and standard deviation.20. The system of claim 15, wherein each feature value in the normalizedvector of feature values is within a range of 0 and 1, inclusive.